Enhancing public sector financial operations and inclusion through innovative Fintech solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This review explores the transformative potential of financial technology (Fintech) in enhancing public sector financial operations and promoting financial inclusion. It examines key Fintech innovations such as automation, blockchain, and artificial intelligence (AI), revolutionizing financial transparency, efficiency, and service delivery in government operations. The paper highlights the challenges of adopting Fintech in the public sector, including regulatory hurdles, technical integration, and data privacy and security concerns. It also identifies emerging trends such as AI, machine learning, and blockchain that offer significant opportunities for public sector growth and digital transformation. Policy recommendations are provided to support Fintech adoption, emphasizing the need for strategic public-private partnerships to ensure sustainable implementation and scalability. Ultimately, this review underscores the critical role of Fintech in modernizing public financial systems, improving operational efficiency, and fostering inclusive access to government financial services. Keywords: Fintech, Public Sector Financial Management, Blockchain, Financial Inclusion, Artificial Intelligence, Digital Transformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it